Improving the design of heart failure care from the perspective of frontline providers and administrators: A qualitative case study of a large, urban health system
Bibliographic record
Abstract
BACKGROUND: Heart failure patients often present with frailty and/or multi-morbidity, complicating care and service delivery. The Chronic Care Model (CCM) is a useful framework for designing care for complex patients. It assumes responsibility of several actors, including frontline providers and health-care administrators, in creating conditions for optimal chronic care management. This qualitative case study examines perceptions of care among providers and administrators in a large, urban health system in Canada, and how the CCM might inform redesign of care to improve health system functioning. METHODS: Sixteen semi-structured interviews were conducted between August 2014 and January 2016. Interpretive analysis was conducted to identify how informants perceive care among this population and the extent to which the design of heart failure care aligns with elements of the CCM. RESULTS: Current care approaches could better align with CCM elements. Key changes to improve health system functioning for complex heart failure patients that align with the CCM include closing knowledge gaps, standardizing treatment, improving interdisciplinary communication and improving patient care pathways following hospital discharge. CONCLUSIONS: The CCM can be used to guide health system design and interventions for frail and multi-morbid heart failure patients. Addressing care- and service-delivery barriers has important clinical, administrative and economic implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".